Global occlusion self-adaptive pedestrian training/recognition method, system and device and medium
A training method and pedestrian recognition technology, applied in the field of image recognition, can solve problems such as no intuitive application, achieve high industrial utilization value, reduce labor costs, and improve efficiency
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Embodiment 1
[0050] This embodiment provides a global occlusion adaptive pedestrian training method, including:
[0051] Receive a training data set; the training data set includes N pedestrians, each pedestrian has M pictures; wherein, N is greater than 1, and M is greater than 1;
[0052] Extract the attribute feature map of each picture to output the NxM attribute feature map;
[0053] Fuse all the attribute feature maps of the same pedestrian to form the fusion feature of the pedestrian, and obtain the fusion features of several pedestrians;
[0054] extracting local features from the plurality of attribute feature maps and extracting global features from the fusion features of the plurality of pedestrians;
[0055] According to the local feature and the global feature, the attention of the local feature is extracted to calculate the local feature attention-enhanced feature used to characterize the local feature and the global feature used to characterize the global feature. The feat...
Embodiment 2
[0133] This embodiment provides a global occlusion adaptive pedestrian training system, which is characterized in that it includes:
[0134] The data receiving module is used to receive a training data set; the training data set includes N pedestrians, and each pedestrian has M pictures; wherein, N is greater than 1, and M is greater than 1;
[0135] The first feature extraction module is used to extract the attribute feature map of each picture, so as to output the NxM attribute feature map;
[0136] The fusion module is used to fuse all the attribute feature maps of the same pedestrian to form the fusion feature of the pedestrian, and obtain the fusion features of several pedestrians;
[0137] The second feature extraction module is used to extract local features from the multiple attribute feature maps and extract global features from the fusion features of the plurality of pedestrians;
[0138]The attention extraction module is used to extract the attention of the local f...
specific Embodiment
[0154] Step 1: The data receiving module collects images of pedestrians, or downloads public datasets for pedestrian re-identification; divides the datasets into training sets and test sets; the Market1501 public datasets are used in this invention.
[0155] Step 2: The first feature extraction module is loaded into the VGG-16 network, and the initial weight is the pre-training weight of VGG-16 on ImageNet; for the second feature extraction module and the convolution layer in the attention extraction module, batch normalization The weights are initialized with a normal distribution with a mean value of 0 and a mean square error of 0.01, and the bias is initialized with 0. The alpha parameter value in TripletLoss is set to 0.3.
[0156] Step 3: Input data and train the network. Each batch of data includes 16 pedestrians, and each pedestrian has 4 images. The training is carried out for 100 epochs in total, the initial learning rate is set to 0.002, and the learning rate is mu...
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